Inferring Drug-Target Interactions Using Graph Isomorphic Network and Word Vector Matrix

Computer-aided drug discovery can efficiently predict drug-target interactions, which helps biological researchers to narrow down the search space and reduce experimental consumption. However, the accuracy of existing prediction methods still needs to be further improved. This paper proposes a deep learning model that predicts drug-target interactions through effective strategies: the graphical representation of SMILES based on Graph Isomorphic Network (GIN) and word vector matrices of amino acid sequences based on one-dimensional convolution, which is used to extract features of drug-target pairs for classification prediction. Compared with the previous methods, the proposed method outperforms the existing work with AUC. A case study of predicting COVID-19 DTIs also shows that the proposed method can be invoked to be use for practical drug-target prediction.

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